{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Classification Cluster Part 3"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# fix_yahoo_finance is used to fetch data \n",
        "import fix_yahoo_finance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stocks_dict = {\n",
        "    'Advanced Micro Devices':'AMD',\n",
        "    'Amazon': 'AMZN',\n",
        "    'Apple': 'AAPL',\n",
        "    'Walgreen': 'WBA',\n",
        "    'Northrop Grumman': 'NOC',\n",
        "    'Boeing': 'BA',\n",
        "    'Lockheed Martin': 'LMT',\n",
        "    'McDonalds':  'MCD',\n",
        "    'Intel': 'INTC',\n",
        "    'Navistar': 'NAV',\n",
        "    'IBM': 'IBM',\n",
        "    'Texas Instruments': 'TXN',\n",
        "    'MasterCard': 'MA',\n",
        "    'Microsoft': 'MSFT',\n",
        "    'General Electrics': 'GE',\n",
        "    'Symantec': 'SYMC',\n",
        "    'American Express': 'AXP',\n",
        "    'Pepsi': 'PEP',\n",
        "    'Coca Cola': 'KO',\n",
        "    'Johnson & Johnson': 'JNJ',\n",
        "    'Toyota': 'TM',\n",
        "    'Honda': 'HMC',\n",
        "    'Mistubishi': 'MSBHY',\n",
        "    'Sony': 'SNE',\n",
        "    'Exxon': 'XOM',\n",
        "    'Chevron': 'CVX',\n",
        "    'Valero Energy': 'VLO',\n",
        "    'Ford': 'F',\n",
        "    'Bank of America': 'BAC', \n",
        "    'Petrobras': 'PBR',\n",
        "    'Vale': 'VALE'}"
      ],
      "outputs": [],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split dict into list of companies and symbols\n",
        "names, symbols = np.array(list(stocks_dict.items())).T"
      ],
      "outputs": [],
      "execution_count": 3,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "start = '2017-01-01'\n",
        "end = '2017-12-31'\n",
        "\n",
        "print(\"Downloading values for period %s to %s:\" % (start, end))\n",
        "dataset = [yf.download(symbol,start,end) for symbol in symbols]\n",
        "print(\"Done!\")"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading values for period 2017-01-01 to 2017-12-31:\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "[*********************100%***********************]  1 of 1 downloaded\n",
            "Done!\n"
          ]
        }
      ],
      "execution_count": 5,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "market_dates = np.vstack([dataset.index])\n",
        "open_price = np.array([p[\"Open\"] for p in dataset]).astype(np.float)\n",
        "high_price = np.array([p[\"High\"] for p in dataset]).astype(np.float)\n",
        "low_price = np.array([p[\"Low\"] for p in dataset]).astype(np.float)\n",
        "close_price = np.array([p[\"Adj Close\"] for p in dataset]).astype(np.float)\n",
        "volume_price = np.array([p[\"Volume\"] for p in dataset]).astype(np.float)"
      ],
      "outputs": [],
      "execution_count": 29,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate percent change\n",
        "X = (close_price - open_price) / open_price"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# The daily variations of the quotes are what carry most information\n",
        "variation = close_price - open_price"
      ],
      "outputs": [],
      "execution_count": 32,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import cluster, covariance, manifold\n",
        "\n",
        "# Learn a graphical structure from the correlations\n",
        "edge_model = covariance.GraphLassoCV()\n",
        "\n",
        "# standardize the time series: using correlations rather than covariance\n",
        "# is more efficient for structure recovery\n",
        "X = variation.copy().T\n",
        "X /= X.std(axis=0)\n",
        "edge_model.fit(X)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 33,
          "data": {
            "text/plain": [
              "GraphLassoCV(alphas=4, assume_centered=False, cv=None, enet_tol=0.0001,\n",
              "       max_iter=100, mode='cd', n_jobs=1, n_refinements=4, tol=0.0001,\n",
              "       verbose=False)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 33,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Cluster using affinity propagation\n",
        "\n",
        "_, labels = cluster.affinity_propagation(edge_model.covariance_)\n",
        "n_labels = labels.max()\n",
        "\n",
        "for i in range(n_labels + 1):\n",
        "    print('Cluster %i: %s' % ((i + 1), ', '.join(names[labels == i])))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cluster 1: Microsoft, McDonalds, MasterCard, Symantec, Texas Instruments\n",
            "Cluster 2: Lockheed Martin, Northrop Grumman, Boeing\n",
            "Cluster 3: Honda, Ford, Exxon, Intel, Chevron, Pepsi, Coca Cola, Toyota, Walgreen, IBM, Mistubishi, General Electrics, Valero Energy, Johnson & Johnson\n",
            "Cluster 4: Navistar\n",
            "Cluster 5: Advanced Micro Devices\n",
            "Cluster 6: Bank of America, American Express\n",
            "Cluster 7: Petrobras, Vale\n",
            "Cluster 8: Sony, Amazon, Apple\n"
          ]
        }
      ],
      "execution_count": 34,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Find a low-dimension embedding for visualization: find the best position of\n",
        "# the nodes (the stocks) on a 2D plane\n",
        "\n",
        "# We use a dense eigen_solver to achieve reproducibility (arpack is\n",
        "# initiated with random vectors that we don't control). In addition, we\n",
        "# use a large number of neighbors to capture the large-scale structure.\n",
        "node_position_model = manifold.LocallyLinearEmbedding(n_components=2, eigen_solver='dense', n_neighbors=6)\n",
        "\nembedding = node_position_model.fit_transform(X.T).T"
      ],
      "outputs": [],
      "execution_count": 35,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# #############################################################################\n",
        "import matplotlib.cm as cm\n",
        "cmap = cm.get_cmap(\"Spectral\")\n",
        "\n",
        "# Visualization\n",
        "plt.figure(1, facecolor='w', figsize=(10, 8))\n",
        "plt.clf()\n",
        "ax = plt.axes([0., 0., 1., 1.])\n",
        "plt.axis('off')\n",
        "\n",
        "# Display a graph of the partial correlations\n",
        "partial_correlations = edge_model.precision_.copy()\n",
        "d = 1 / np.sqrt(np.diag(partial_correlations))\n",
        "partial_correlations *= d\n",
        "partial_correlations *= d[:, np.newaxis]\n",
        "non_zero = (np.abs(np.triu(partial_correlations, k=1)) > 0.02)\n",
        "\n",
        "# Plot the nodes using the coordinates of our embedding\n",
        "plt.scatter(embedding[0], embedding[1], s=100 * d ** 2, c=labels,\n",
        "            cmap=cmap)\n",
        "\n",
        "# Plot the edges\n",
        "start_idx, end_idx = np.where(non_zero)\n",
        "# a sequence of (*line0*, *line1*, *line2*), where::\n",
        "#            linen = (x0, y0), (x1, y1), ... (xm, ym)\n",
        "segments = [[embedding[:, start], embedding[:, stop]]\n",
        "            for start, stop in zip(start_idx, end_idx)]\n",
        "values = np.abs(partial_correlations[non_zero])\n",
        "lc = LineCollection(segments,\n",
        "                    zorder=0, cmap=plt.cm.hot_r,\n",
        "                    norm=plt.Normalize(0, .7 * values.max()))\n",
        "lc.set_array(values)\n",
        "lc.set_linewidths(15 * values)\n",
        "ax.add_collection(lc)\n",
        "\n",
        "# Add a label to each node. The challenge here is that we want to\n",
        "# position the labels to avoid overlap with other labels\n",
        "for index, (name, label, (x, y)) in enumerate(\n",
        "        zip(names, labels, embedding.T)):\n",
        "\n",
        "    dx = x - embedding[0]\n",
        "    dx[index] = 1\n",
        "    dy = y - embedding[1]\n",
        "    dy[index] = 1\n",
        "    this_dx = dx[np.argmin(np.abs(dy))]\n",
        "    this_dy = dy[np.argmin(np.abs(dx))]\n",
        "    if this_dx > 0:\n",
        "        horizontalalignment = 'left'\n",
        "        x = x + .002\n",
        "    else:\n",
        "        horizontalalignment = 'right'\n",
        "        x = x - .002\n",
        "    if this_dy > 0:\n",
        "        verticalalignment = 'bottom'\n",
        "        y = y + .002\n",
        "    else:\n",
        "        verticalalignment = 'top'\n",
        "        y = y - .002\n",
        "    plt.text(x, y, name, size=10,\n",
        "             horizontalalignment=horizontalalignment,\n",
        "             verticalalignment=verticalalignment,\n",
        "             bbox=dict(facecolor='w',\n",
        "                       edgecolor=cmap(label / float(n_labels)),\n",
        "                       alpha=.6))\n",
        "\n",
        "plt.xlim(embedding[0].min() - .15 * embedding[0].ptp(),\n",
        "         embedding[0].max() + .10 * embedding[0].ptp(),)\n",
        "plt.ylim(embedding[1].min() - .03 * embedding[1].ptp(),\n",
        "         embedding[1].max() + .03 * embedding[1].ptp())\n",
        "\nplt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": [
              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\n"
            ],
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 48,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "file_extension": ".py",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "version": "3.5.5",
      "mimetype": "text/x-python"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "nteract": {
      "version": "0.11.9"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}